Recently, due to the advancement in robot technologies, mobile robots that set a path and move by itself have been utilized. Representative examples of mobile robots include a cleaning robot that cleans houses or buildings and a receptionist robot that helps to find a location. Especially, the cleaning robot includes various sensors and driving units to clean indoor floors using a vacuum cleaning unit provided therein while traveling. Currently, various cleaning robots are currently used.
In order to efficiently judge the position and move the mobile robot, the mobile robot is required to generate a map for the space where the robot is moving and recognize the location in the space. Simultaneous localization and mapping (SLAM) refers that the mobile robot recognizes the location in the surrounding space and generates a map.
Among the SLAM techniques, an image based SLAM generates the map regarding the circumference environment using a visual feature point extracted from an image and estimates a posture of the robot. Generally, the mobile robot is driven by a dead reckoning method using a gyroscope and an encoder provided in a driving motor. Further, the mobile robot uses a camera provided at the upper part to analyze an image and generate a map. In this case, if there is an error in driving information generated by the gyroscope and the encoder, the accumulated error is corrected by using image information obtained from the camera.
Until now, even though various related arts regarding a mobile robot driving control method and a mobile robot using the same are suggested, the following problems have not been solved.
Even though various methods are used as a descriptor for a feature point extracted from the image, excellent performance cannot be achieved in a case when change in the illumination or image is significant. Further, when the driving of the mobile robot is controlled, if the input image cannot be used or an error occurs in the driving control of the mobile robot, an adaptive driving control method cannot be suggested. Furthermore, in the feature point matching, wrong feature points are recognized as the same feature points.